Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations212354
Missing cells42486
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.0 MiB
Average record size in memory232.0 B

Variable types

Numeric13
Categorical16

Alerts

biopsy_results is highly imbalanced (53.3%) Imbalance
label is highly imbalanced (53.5%) Imbalance
existing_conditions has 42486 (20.0%) missing values Missing
diana_microt has unique values Unique
elmmo has unique values Unique
microcosm has unique values Unique
miranda has unique values Unique
mirdb has unique values Unique
pictar has unique values Unique
pita has unique values Unique
targetscan has unique values Unique
predicted.sum has unique values Unique
all.sum has unique values Unique

Reproduction

Analysis started2025-02-23 06:41:54.969601
Analysis finished2025-02-23 06:42:28.844547
Duration33.87 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.258036
Minimum20
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:28.971006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile26
Q137
median50
Q369
95-th percentile85
Maximum89
Range69
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.984419
Coefficient of variation (CV)0.35646112
Kurtosis-1.0959819
Mean53.258036
Median Absolute Deviation (MAD)15
Skewness0.24603705
Sum11309557
Variance360.40817
MonotonicityNot monotonic
2025-02-23T12:12:29.186558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 4549
 
2.1%
34 4518
 
2.1%
42 4505
 
2.1%
33 4492
 
2.1%
36 4480
 
2.1%
37 4475
 
2.1%
47 4464
 
2.1%
30 4457
 
2.1%
46 4417
 
2.1%
48 4408
 
2.1%
Other values (60) 167589
78.9%
ValueCountFrequency (%)
20 1755
0.8%
21 1762
0.8%
22 1724
0.8%
23 1707
0.8%
24 1795
0.8%
25 1750
0.8%
26 1817
0.9%
27 1820
0.9%
28 1860
0.9%
29 1736
0.8%
ValueCountFrequency (%)
89 2645
1.2%
88 2563
1.2%
87 2717
1.3%
86 2575
1.2%
85 2609
1.2%
84 2712
1.3%
83 2630
1.2%
82 2615
1.2%
81 2673
1.3%
80 2710
1.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Male
148706 
Female
63648 

Length

Max length6
Median length4
Mean length4.5994519
Min length4

Characters and Unicode

Total characters976712
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 148706
70.0%
Female 63648
30.0%

Length

2025-02-23T12:12:29.458320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:29.539573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 148706
70.0%
female 63648
30.0%

Most occurring characters

ValueCountFrequency (%)
e 276002
28.3%
a 212354
21.7%
l 212354
21.7%
M 148706
15.2%
F 63648
 
6.5%
m 63648
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 976712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 276002
28.3%
a 212354
21.7%
l 212354
21.7%
M 148706
15.2%
F 63648
 
6.5%
m 63648
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 976712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 276002
28.3%
a 212354
21.7%
l 212354
21.7%
M 148706
15.2%
F 63648
 
6.5%
m 63648
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 976712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 276002
28.3%
a 212354
21.7%
l 212354
21.7%
M 148706
15.2%
F 63648
 
6.5%
m 63648
 
6.5%

ethnicity
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Ethnicity_A
127571 
Ethnicity_B
63569 
Ethnicity_C
21214 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2335894
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEthnicity_A
2nd rowEthnicity_B
3rd rowEthnicity_A
4th rowEthnicity_A
5th rowEthnicity_A

Common Values

ValueCountFrequency (%)
Ethnicity_A 127571
60.1%
Ethnicity_B 63569
29.9%
Ethnicity_C 21214
 
10.0%

Length

2025-02-23T12:12:29.630672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:29.707819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ethnicity_a 127571
60.1%
ethnicity_b 63569
29.9%
ethnicity_c 21214
 
10.0%

Most occurring characters

ValueCountFrequency (%)
t 424708
18.2%
i 424708
18.2%
E 212354
9.1%
h 212354
9.1%
n 212354
9.1%
c 212354
9.1%
y 212354
9.1%
_ 212354
9.1%
A 127571
 
5.5%
B 63569
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2335894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 424708
18.2%
i 424708
18.2%
E 212354
9.1%
h 212354
9.1%
n 212354
9.1%
c 212354
9.1%
y 212354
9.1%
_ 212354
9.1%
A 127571
 
5.5%
B 63569
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2335894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 424708
18.2%
i 424708
18.2%
E 212354
9.1%
h 212354
9.1%
n 212354
9.1%
c 212354
9.1%
y 212354
9.1%
_ 212354
9.1%
A 127571
 
5.5%
B 63569
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2335894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 424708
18.2%
i 424708
18.2%
E 212354
9.1%
h 212354
9.1%
n 212354
9.1%
c 212354
9.1%
y 212354
9.1%
_ 212354
9.1%
A 127571
 
5.5%
B 63569
 
2.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
California
169603 
Other
42751 

Length

Max length10
Median length10
Mean length8.9934025
Min length5

Characters and Unicode

Total characters1909785
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowCalifornia
3rd rowCalifornia
4th rowOther
5th rowCalifornia

Common Values

ValueCountFrequency (%)
California 169603
79.9%
Other 42751
 
20.1%

Length

2025-02-23T12:12:29.807082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:29.875566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
california 169603
79.9%
other 42751
 
20.1%

Most occurring characters

ValueCountFrequency (%)
a 339206
17.8%
i 339206
17.8%
r 212354
11.1%
C 169603
8.9%
l 169603
8.9%
f 169603
8.9%
o 169603
8.9%
n 169603
8.9%
O 42751
 
2.2%
t 42751
 
2.2%
Other values (2) 85502
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1909785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 339206
17.8%
i 339206
17.8%
r 212354
11.1%
C 169603
8.9%
l 169603
8.9%
f 169603
8.9%
o 169603
8.9%
n 169603
8.9%
O 42751
 
2.2%
t 42751
 
2.2%
Other values (2) 85502
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1909785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 339206
17.8%
i 339206
17.8%
r 212354
11.1%
C 169603
8.9%
l 169603
8.9%
f 169603
8.9%
o 169603
8.9%
n 169603
8.9%
O 42751
 
2.2%
t 42751
 
2.2%
Other values (2) 85502
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1909785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 339206
17.8%
i 339206
17.8%
r 212354
11.1%
C 169603
8.9%
l 169603
8.9%
f 169603
8.9%
o 169603
8.9%
n 169603
8.9%
O 42751
 
2.2%
t 42751
 
2.2%
Other values (2) 85502
 
4.5%

family_history
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
148528 
1
63826 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters212354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 148528
69.9%
1 63826
30.1%

Length

2025-02-23T12:12:29.951821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:30.012762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 148528
69.9%
1 63826
30.1%

Most occurring characters

ValueCountFrequency (%)
0 148528
69.9%
1 63826
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 148528
69.9%
1 63826
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 148528
69.9%
1 63826
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 148528
69.9%
1 63826
30.1%

smoking_habits
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
127592 
1
84762 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters212354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 127592
60.1%
1 84762
39.9%

Length

2025-02-23T12:12:30.074142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:30.134888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 127592
60.1%
1 84762
39.9%

Most occurring characters

ValueCountFrequency (%)
0 127592
60.1%
1 84762
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 127592
60.1%
1 84762
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 127592
60.1%
1 84762
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 127592
60.1%
1 84762
39.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
106309 
1
106045 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters212354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 106309
50.1%
1 106045
49.9%

Length

2025-02-23T12:12:30.214508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:30.272702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 106309
50.1%
1 106045
49.9%

Most occurring characters

ValueCountFrequency (%)
0 106309
50.1%
1 106045
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 106309
50.1%
1 106045
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 106309
50.1%
1 106045
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 106309
50.1%
1 106045
49.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
159378 
1
52976 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters212354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 159378
75.1%
1 52976
 
24.9%

Length

2025-02-23T12:12:30.348727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:30.408043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 159378
75.1%
1 52976
 
24.9%

Most occurring characters

ValueCountFrequency (%)
0 159378
75.1%
1 52976
 
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 159378
75.1%
1 52976
 
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 159378
75.1%
1 52976
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 159378
75.1%
1 52976
 
24.9%

dietary_habits
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
High_Salt
169805 
Low_Salt
42549 

Length

Max length9
Median length9
Mean length8.7996317
Min length8

Characters and Unicode

Total characters1868637
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow_Salt
2nd rowHigh_Salt
3rd rowHigh_Salt
4th rowHigh_Salt
5th rowHigh_Salt

Common Values

ValueCountFrequency (%)
High_Salt 169805
80.0%
Low_Salt 42549
 
20.0%

Length

2025-02-23T12:12:30.493466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:30.557910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high_salt 169805
80.0%
low_salt 42549
 
20.0%

Most occurring characters

ValueCountFrequency (%)
l 212354
11.4%
a 212354
11.4%
S 212354
11.4%
_ 212354
11.4%
t 212354
11.4%
g 169805
9.1%
i 169805
9.1%
H 169805
9.1%
h 169805
9.1%
L 42549
 
2.3%
Other values (2) 85098
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1868637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 212354
11.4%
a 212354
11.4%
S 212354
11.4%
_ 212354
11.4%
t 212354
11.4%
g 169805
9.1%
i 169805
9.1%
H 169805
9.1%
h 169805
9.1%
L 42549
 
2.3%
Other values (2) 85098
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1868637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 212354
11.4%
a 212354
11.4%
S 212354
11.4%
_ 212354
11.4%
t 212354
11.4%
g 169805
9.1%
i 169805
9.1%
H 169805
9.1%
h 169805
9.1%
L 42549
 
2.3%
Other values (2) 85098
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1868637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 212354
11.4%
a 212354
11.4%
S 212354
11.4%
_ 212354
11.4%
t 212354
11.4%
g 169805
9.1%
i 169805
9.1%
H 169805
9.1%
h 169805
9.1%
L 42549
 
2.3%
Other values (2) 85098
4.6%

existing_conditions
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing42486
Missing (%)20.0%
Memory size1.6 MiB
Chronic Gastritis
106309 
Diabetes
63559 

Length

Max length17
Median length17
Mean length13.632497
Min length8

Characters and Unicode

Total characters2315725
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChronic Gastritis
2nd rowDiabetes
3rd rowChronic Gastritis
4th rowDiabetes
5th rowChronic Gastritis

Common Values

ValueCountFrequency (%)
Chronic Gastritis 106309
50.1%
Diabetes 63559
29.9%
(Missing) 42486
 
20.0%

Length

2025-02-23T12:12:30.633940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:30.699373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
chronic 106309
38.5%
gastritis 106309
38.5%
diabetes 63559
23.0%

Most occurring characters

ValueCountFrequency (%)
i 382486
16.5%
t 276177
11.9%
s 276177
11.9%
r 212618
9.2%
a 169868
 
7.3%
e 127118
 
5.5%
C 106309
 
4.6%
c 106309
 
4.6%
n 106309
 
4.6%
o 106309
 
4.6%
Other values (5) 446045
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2315725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 382486
16.5%
t 276177
11.9%
s 276177
11.9%
r 212618
9.2%
a 169868
 
7.3%
e 127118
 
5.5%
C 106309
 
4.6%
c 106309
 
4.6%
n 106309
 
4.6%
o 106309
 
4.6%
Other values (5) 446045
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2315725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 382486
16.5%
t 276177
11.9%
s 276177
11.9%
r 212618
9.2%
a 169868
 
7.3%
e 127118
 
5.5%
C 106309
 
4.6%
c 106309
 
4.6%
n 106309
 
4.6%
o 106309
 
4.6%
Other values (5) 446045
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2315725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 382486
16.5%
t 276177
11.9%
s 276177
11.9%
r 212618
9.2%
a 169868
 
7.3%
e 127118
 
5.5%
C 106309
 
4.6%
c 106309
 
4.6%
n 106309
 
4.6%
o 106309
 
4.6%
Other values (5) 446045
19.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Normal
148523 
Abnormal
63831 

Length

Max length8
Median length6
Mean length6.6011754
Min length6

Characters and Unicode

Total characters1401786
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowAbnormal

Common Values

ValueCountFrequency (%)
Normal 148523
69.9%
Abnormal 63831
30.1%

Length

2025-02-23T12:12:30.788725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:30.852992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 148523
69.9%
abnormal 63831
30.1%

Most occurring characters

ValueCountFrequency (%)
o 212354
15.1%
l 212354
15.1%
r 212354
15.1%
m 212354
15.1%
a 212354
15.1%
N 148523
10.6%
A 63831
 
4.6%
b 63831
 
4.6%
n 63831
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1401786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 212354
15.1%
l 212354
15.1%
r 212354
15.1%
m 212354
15.1%
a 212354
15.1%
N 148523
10.6%
A 63831
 
4.6%
b 63831
 
4.6%
n 63831
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1401786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 212354
15.1%
l 212354
15.1%
r 212354
15.1%
m 212354
15.1%
a 212354
15.1%
N 148523
10.6%
A 63831
 
4.6%
b 63831
 
4.6%
n 63831
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1401786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 212354
15.1%
l 212354
15.1%
r 212354
15.1%
m 212354
15.1%
a 212354
15.1%
N 148523
10.6%
A 63831
 
4.6%
b 63831
 
4.6%
n 63831
 
4.6%

biopsy_results
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Negative
191223 
Positive
21131 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1698832
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowNegative
3rd rowNegative
4th rowNegative
5th rowNegative

Common Values

ValueCountFrequency (%)
Negative 191223
90.0%
Positive 21131
 
10.0%

Length

2025-02-23T12:12:30.944245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:31.010309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
negative 191223
90.0%
positive 21131
 
10.0%

Most occurring characters

ValueCountFrequency (%)
e 403577
23.8%
i 233485
13.7%
v 212354
12.5%
t 212354
12.5%
N 191223
11.3%
a 191223
11.3%
g 191223
11.3%
P 21131
 
1.2%
o 21131
 
1.2%
s 21131
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 403577
23.8%
i 233485
13.7%
v 212354
12.5%
t 212354
12.5%
N 191223
11.3%
a 191223
11.3%
g 191223
11.3%
P 21131
 
1.2%
o 21131
 
1.2%
s 21131
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 403577
23.8%
i 233485
13.7%
v 212354
12.5%
t 212354
12.5%
N 191223
11.3%
a 191223
11.3%
g 191223
11.3%
P 21131
 
1.2%
o 21131
 
1.2%
s 21131
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 403577
23.8%
i 233485
13.7%
v 212354
12.5%
t 212354
12.5%
N 191223
11.3%
a 191223
11.3%
g 191223
11.3%
P 21131
 
1.2%
o 21131
 
1.2%
s 21131
 
1.2%

ct_scan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Negative
169740 
Positive
42614 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1698832
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowNegative
3rd rowNegative
4th rowNegative
5th rowNegative

Common Values

ValueCountFrequency (%)
Negative 169740
79.9%
Positive 42614
 
20.1%

Length

2025-02-23T12:12:31.096387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:31.159909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
negative 169740
79.9%
positive 42614
 
20.1%

Most occurring characters

ValueCountFrequency (%)
e 382094
22.5%
i 254968
15.0%
v 212354
12.5%
t 212354
12.5%
N 169740
10.0%
a 169740
10.0%
g 169740
10.0%
P 42614
 
2.5%
o 42614
 
2.5%
s 42614
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 382094
22.5%
i 254968
15.0%
v 212354
12.5%
t 212354
12.5%
N 169740
10.0%
a 169740
10.0%
g 169740
10.0%
P 42614
 
2.5%
o 42614
 
2.5%
s 42614
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 382094
22.5%
i 254968
15.0%
v 212354
12.5%
t 212354
12.5%
N 169740
10.0%
a 169740
10.0%
g 169740
10.0%
P 42614
 
2.5%
o 42614
 
2.5%
s 42614
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 382094
22.5%
i 254968
15.0%
v 212354
12.5%
t 212354
12.5%
N 169740
10.0%
a 169740
10.0%
g 169740
10.0%
P 42614
 
2.5%
o 42614
 
2.5%
s 42614
 
2.5%

mature_mirna_acc
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
MIR123
148596 
MIR234
42474 
MIR345
21284 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1274124
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIR123
2nd rowMIR123
3rd rowMIR345
4th rowMIR123
5th rowMIR345

Common Values

ValueCountFrequency (%)
MIR123 148596
70.0%
MIR234 42474
 
20.0%
MIR345 21284
 
10.0%

Length

2025-02-23T12:12:31.248086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:31.304678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mir123 148596
70.0%
mir234 42474
 
20.0%
mir345 21284
 
10.0%

Most occurring characters

ValueCountFrequency (%)
M 212354
16.7%
I 212354
16.7%
R 212354
16.7%
3 212354
16.7%
2 191070
15.0%
1 148596
11.7%
4 63758
 
5.0%
5 21284
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1274124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 212354
16.7%
I 212354
16.7%
R 212354
16.7%
3 212354
16.7%
2 191070
15.0%
1 148596
11.7%
4 63758
 
5.0%
5 21284
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1274124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 212354
16.7%
I 212354
16.7%
R 212354
16.7%
3 212354
16.7%
2 191070
15.0%
1 148596
11.7%
4 63758
 
5.0%
5 21284
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1274124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 212354
16.7%
I 212354
16.7%
R 212354
16.7%
3 212354
16.7%
2 191070
15.0%
1 148596
11.7%
4 63758
 
5.0%
5 21284
 
1.7%

mature_mirna_id
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
MIR123_1
148248 
MIR234_2
42780 
MIR345_3
21326 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1698832
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIR123_1
2nd rowMIR234_2
3rd rowMIR345_3
4th rowMIR123_1
5th rowMIR123_1

Common Values

ValueCountFrequency (%)
MIR123_1 148248
69.8%
MIR234_2 42780
 
20.1%
MIR345_3 21326
 
10.0%

Length

2025-02-23T12:12:31.383875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:31.438858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mir123_1 148248
69.8%
mir234_2 42780
 
20.1%
mir345_3 21326
 
10.0%

Most occurring characters

ValueCountFrequency (%)
1 296496
17.5%
2 233808
13.8%
3 233680
13.8%
M 212354
12.5%
R 212354
12.5%
I 212354
12.5%
_ 212354
12.5%
4 64106
 
3.8%
5 21326
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 296496
17.5%
2 233808
13.8%
3 233680
13.8%
M 212354
12.5%
R 212354
12.5%
I 212354
12.5%
_ 212354
12.5%
4 64106
 
3.8%
5 21326
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 296496
17.5%
2 233808
13.8%
3 233680
13.8%
M 212354
12.5%
R 212354
12.5%
I 212354
12.5%
_ 212354
12.5%
4 64106
 
3.8%
5 21326
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1698832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 296496
17.5%
2 233808
13.8%
3 233680
13.8%
M 212354
12.5%
R 212354
12.5%
I 212354
12.5%
_ 212354
12.5%
4 64106
 
3.8%
5 21326
 
1.3%

target_symbol
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
TP53
106351 
CDH1
63570 
KRAS
42433 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters849416
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTP53
2nd rowTP53
3rd rowKRAS
4th rowKRAS
5th rowCDH1

Common Values

ValueCountFrequency (%)
TP53 106351
50.1%
CDH1 63570
29.9%
KRAS 42433
 
20.0%

Length

2025-02-23T12:12:31.521701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:31.595643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tp53 106351
50.1%
cdh1 63570
29.9%
kras 42433
 
20.0%

Most occurring characters

ValueCountFrequency (%)
T 106351
12.5%
P 106351
12.5%
5 106351
12.5%
3 106351
12.5%
C 63570
7.5%
D 63570
7.5%
H 63570
7.5%
1 63570
7.5%
K 42433
 
5.0%
R 42433
 
5.0%
Other values (2) 84866
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 849416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 106351
12.5%
P 106351
12.5%
5 106351
12.5%
3 106351
12.5%
C 63570
7.5%
D 63570
7.5%
H 63570
7.5%
1 63570
7.5%
K 42433
 
5.0%
R 42433
 
5.0%
Other values (2) 84866
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 849416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 106351
12.5%
P 106351
12.5%
5 106351
12.5%
3 106351
12.5%
C 63570
7.5%
D 63570
7.5%
H 63570
7.5%
1 63570
7.5%
K 42433
 
5.0%
R 42433
 
5.0%
Other values (2) 84866
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 849416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 106351
12.5%
P 106351
12.5%
5 106351
12.5%
3 106351
12.5%
C 63570
7.5%
D 63570
7.5%
H 63570
7.5%
1 63570
7.5%
K 42433
 
5.0%
R 42433
 
5.0%
Other values (2) 84866
10.0%

target_entrez
Real number (ℝ)

Distinct9000
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5496.1173
Minimum1000
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:31.711753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1449
Q13252
median5491
Q37738
95-th percentile9547
Maximum9999
Range8999
Interquartile range (IQR)4486

Descriptive statistics

Standard deviation2596.8176
Coefficient of variation (CV)0.4724822
Kurtosis-1.1970572
Mean5496.1173
Median Absolute Deviation (MAD)2243
Skewness0.0011763769
Sum1.1671225 × 109
Variance6743461.8
MonotonicityNot monotonic
2025-02-23T12:12:31.848338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5737 44
 
< 0.1%
8008 43
 
< 0.1%
1341 43
 
< 0.1%
3530 42
 
< 0.1%
6519 41
 
< 0.1%
4279 40
 
< 0.1%
1046 40
 
< 0.1%
4050 40
 
< 0.1%
9428 40
 
< 0.1%
7616 40
 
< 0.1%
Other values (8990) 211941
99.8%
ValueCountFrequency (%)
1000 30
< 0.1%
1001 22
< 0.1%
1002 26
< 0.1%
1003 19
< 0.1%
1004 21
< 0.1%
1005 27
< 0.1%
1006 28
< 0.1%
1007 32
< 0.1%
1008 24
< 0.1%
1009 25
< 0.1%
ValueCountFrequency (%)
9999 25
< 0.1%
9998 23
< 0.1%
9997 23
< 0.1%
9996 20
< 0.1%
9995 27
< 0.1%
9994 28
< 0.1%
9993 24
< 0.1%
9992 27
< 0.1%
9991 21
< 0.1%
9990 22
< 0.1%

target_ensembl
Real number (ℝ)

Distinct191184
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499874
Minimum1000007
Maximum1999998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:32.006003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000007
5-th percentile1049352.6
Q11251177
median1500323.5
Q31748423.5
95-th percentile1949640.4
Maximum1999998
Range999991
Interquartile range (IQR)497246.5

Descriptive statistics

Standard deviation288258.53
Coefficient of variation (CV)0.19218849
Kurtosis-1.193807
Mean1499874
Median Absolute Deviation (MAD)248674.5
Skewness-0.0013157608
Sum3.1850425 × 1011
Variance8.3092979 × 1010
MonotonicityNot monotonic
2025-02-23T12:12:32.303657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1108334 5
 
< 0.1%
1621017 5
 
< 0.1%
1228716 5
 
< 0.1%
1686987 5
 
< 0.1%
1471937 5
 
< 0.1%
1396220 5
 
< 0.1%
1307868 4
 
< 0.1%
1567669 4
 
< 0.1%
1465615 4
 
< 0.1%
1969936 4
 
< 0.1%
Other values (191174) 212308
> 99.9%
ValueCountFrequency (%)
1000007 1
< 0.1%
1000009 1
< 0.1%
1000012 1
< 0.1%
1000014 1
< 0.1%
1000029 1
< 0.1%
1000030 1
< 0.1%
1000038 1
< 0.1%
1000039 1
< 0.1%
1000048 1
< 0.1%
1000058 1
< 0.1%
ValueCountFrequency (%)
1999998 1
< 0.1%
1999994 1
< 0.1%
1999987 1
< 0.1%
1999979 2
< 0.1%
1999977 1
< 0.1%
1999976 1
< 0.1%
1999964 2
< 0.1%
1999957 1
< 0.1%
1999954 1
< 0.1%
1999951 1
< 0.1%

diana_microt
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50052688
Minimum5.4594294 × 10-6
Maximum0.99999837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:32.489577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.4594294 × 10-6
5-th percentile0.050329547
Q10.25003383
median0.50130179
Q30.75078292
95-th percentile0.95008032
Maximum0.99999837
Range0.99999291
Interquartile range (IQR)0.50074908

Descriptive statistics

Standard deviation0.28875716
Coefficient of variation (CV)0.5769064
Kurtosis-1.2013047
Mean0.50052688
Median Absolute Deviation (MAD)0.25039258
Skewness-0.0032353963
Sum106288.89
Variance0.083380699
MonotonicityNot monotonic
2025-02-23T12:12:32.667384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1469554288 1
 
< 0.1%
0.3813264408 1
 
< 0.1%
0.7766793261 1
 
< 0.1%
0.7422524583 1
 
< 0.1%
0.3561195763 1
 
< 0.1%
0.728199608 1
 
< 0.1%
0.3698819345 1
 
< 0.1%
0.3578230727 1
 
< 0.1%
0.09172957349 1
 
< 0.1%
0.4705122224 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
5.459429415 × 10-61
< 0.1%
1.378755261 × 10-51
< 0.1%
1.544225826 × 10-51
< 0.1%
1.603319656 × 10-51
< 0.1%
1.683744661 × 10-51
< 0.1%
1.903288069 × 10-51
< 0.1%
2.492624933 × 10-51
< 0.1%
3.036230069 × 10-51
< 0.1%
3.428841101 × 10-51
< 0.1%
4.738306033 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999983666 1
< 0.1%
0.9999924808 1
< 0.1%
0.9999890822 1
< 0.1%
0.9999877113 1
< 0.1%
0.9999828503 1
< 0.1%
0.9999783812 1
< 0.1%
0.9999769527 1
< 0.1%
0.9999731289 1
< 0.1%
0.9999668912 1
< 0.1%
0.9999661912 1
< 0.1%

elmmo
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49949712
Minimum6.0890625 × 10-7
Maximum0.9999993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:32.785125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.0890625 × 10-7
5-th percentile0.049287588
Q10.24864684
median0.49980396
Q30.74958433
95-th percentile0.95025324
Maximum0.9999993
Range0.99999869
Interquartile range (IQR)0.50093749

Descriptive statistics

Standard deviation0.28884517
Coefficient of variation (CV)0.57827194
Kurtosis-1.2006813
Mean0.49949712
Median Absolute Deviation (MAD)0.25045947
Skewness0.0023044896
Sum106070.21
Variance0.083431534
MonotonicityNot monotonic
2025-02-23T12:12:32.906515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6896956352 1
 
< 0.1%
0.1870025301 1
 
< 0.1%
0.6596272982 1
 
< 0.1%
0.5951774949 1
 
< 0.1%
0.4263553566 1
 
< 0.1%
0.6901327169 1
 
< 0.1%
0.7974740682 1
 
< 0.1%
0.3561960374 1
 
< 0.1%
0.2488704074 1
 
< 0.1%
0.009788746696 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
6.089062459 × 10-71
< 0.1%
6.997383498 × 10-61
< 0.1%
7.858921587 × 10-61
< 0.1%
1.303431662 × 10-51
< 0.1%
2.214603532 × 10-51
< 0.1%
2.447602945 × 10-51
< 0.1%
2.987345433 × 10-51
< 0.1%
3.18633731 × 10-51
< 0.1%
4.070462222 × 10-51
< 0.1%
4.208724283 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999993036 1
< 0.1%
0.9999955035 1
< 0.1%
0.9999914694 1
< 0.1%
0.999988929 1
< 0.1%
0.9999880918 1
< 0.1%
0.9999788182 1
< 0.1%
0.9999704213 1
< 0.1%
0.9999562781 1
< 0.1%
0.9999518208 1
< 0.1%
0.999949995 1
< 0.1%

microcosm
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50008326
Minimum2.8074476 × 10-6
Maximum0.99999738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:33.019228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.8074476 × 10-6
5-th percentile0.050159621
Q10.24987698
median0.50049801
Q30.74876926
95-th percentile0.95042567
Maximum0.99999738
Range0.99999457
Interquartile range (IQR)0.49889228

Descriptive statistics

Standard deviation0.28849093
Coefficient of variation (CV)0.5768858
Kurtosis-1.1968691
Mean0.50008326
Median Absolute Deviation (MAD)0.2494055
Skewness-0.00043232698
Sum106194.68
Variance0.083227017
MonotonicityNot monotonic
2025-02-23T12:12:33.133894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7015243326 1
 
< 0.1%
0.7864218237 1
 
< 0.1%
0.4906556553 1
 
< 0.1%
0.9975424276 1
 
< 0.1%
0.9297736416 1
 
< 0.1%
0.05440083629 1
 
< 0.1%
0.3709832071 1
 
< 0.1%
0.8738324239 1
 
< 0.1%
0.4456450196 1
 
< 0.1%
0.3298512183 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
2.807447594 × 10-61
< 0.1%
3.632722 × 10-61
< 0.1%
8.553127314 × 10-61
< 0.1%
1.337573821 × 10-51
< 0.1%
1.567621956 × 10-51
< 0.1%
1.681754038 × 10-51
< 0.1%
2.162619995 × 10-51
< 0.1%
2.66833141 × 10-51
< 0.1%
3.497205066 × 10-51
< 0.1%
3.568488452 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999973799 1
< 0.1%
0.9999842374 1
< 0.1%
0.9999836936 1
< 0.1%
0.9999800567 1
< 0.1%
0.9999782066 1
< 0.1%
0.9999752968 1
< 0.1%
0.9999707887 1
< 0.1%
0.9999672694 1
< 0.1%
0.9999646969 1
< 0.1%
0.9999644794 1
< 0.1%

miranda
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4998947
Minimum1.1601668 × 10-6
Maximum0.99999722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:33.248559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1601668 × 10-6
5-th percentile0.049796691
Q10.25020641
median0.49963469
Q30.74907498
95-th percentile0.94954477
Maximum0.99999722
Range0.99999606
Interquartile range (IQR)0.49886857

Descriptive statistics

Standard deviation0.28857114
Coefficient of variation (CV)0.57726385
Kurtosis-1.1987745
Mean0.4998947
Median Absolute Deviation (MAD)0.24943107
Skewness-0.00047022689
Sum106154.64
Variance0.083273302
MonotonicityNot monotonic
2025-02-23T12:12:33.365734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2509549596 1
 
< 0.1%
0.2048164354 1
 
< 0.1%
0.913230403 1
 
< 0.1%
0.6114164536 1
 
< 0.1%
0.5911479985 1
 
< 0.1%
0.5780028225 1
 
< 0.1%
0.1018984975 1
 
< 0.1%
0.3274011811 1
 
< 0.1%
0.2647108839 1
 
< 0.1%
0.4659180306 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
1.160166828 × 10-61
< 0.1%
2.177061828 × 10-61
< 0.1%
5.403161511 × 10-61
< 0.1%
9.935944034 × 10-61
< 0.1%
1.099262032 × 10-51
< 0.1%
1.768943477 × 10-51
< 0.1%
2.100137983 × 10-51
< 0.1%
2.453624145 × 10-51
< 0.1%
2.51735778 × 10-51
< 0.1%
2.678767066 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999972173 1
< 0.1%
0.9999962992 1
< 0.1%
0.9999951188 1
< 0.1%
0.9999932733 1
< 0.1%
0.9999887333 1
< 0.1%
0.9999772986 1
< 0.1%
0.9999726449 1
< 0.1%
0.9999704195 1
< 0.1%
0.9999560305 1
< 0.1%
0.9999533058 1
< 0.1%

mirdb
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50012512
Minimum1.2888397 × 10-5
Maximum0.99999969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:33.490164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.2888397 × 10-5
5-th percentile0.049837593
Q10.25029266
median0.49988755
Q30.7499939
95-th percentile0.94979884
Maximum0.99999969
Range0.9999868
Interquartile range (IQR)0.49970124

Descriptive statistics

Standard deviation0.28873829
Coefficient of variation (CV)0.57733212
Kurtosis-1.1992997
Mean0.50012512
Median Absolute Deviation (MAD)0.24987209
Skewness-0.00081665092
Sum106203.57
Variance0.083369803
MonotonicityNot monotonic
2025-02-23T12:12:33.601250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1024580653 1
 
< 0.1%
0.5619198163 1
 
< 0.1%
0.1394716776 1
 
< 0.1%
0.4179716977 1
 
< 0.1%
0.9407641418 1
 
< 0.1%
0.8902321697 1
 
< 0.1%
0.7633443937 1
 
< 0.1%
0.1189429058 1
 
< 0.1%
0.7810754772 1
 
< 0.1%
0.9661873601 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
1.288839663 × 10-51
< 0.1%
1.735518527 × 10-51
< 0.1%
2.068202303 × 10-51
< 0.1%
2.509233614 × 10-51
< 0.1%
2.621049812 × 10-51
< 0.1%
2.824481228 × 10-51
< 0.1%
3.391776562 × 10-51
< 0.1%
4.578443329 × 10-51
< 0.1%
5.621847435 × 10-51
< 0.1%
6.030695304 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999996932 1
< 0.1%
0.9999984784 1
< 0.1%
0.9999962678 1
< 0.1%
0.9999867404 1
< 0.1%
0.9999852787 1
< 0.1%
0.999977196 1
< 0.1%
0.9999722823 1
< 0.1%
0.9999655619 1
< 0.1%
0.9999590561 1
< 0.1%
0.9999568651 1
< 0.1%

pictar
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49897453
Minimum7.7439575 × 10-7
Maximum0.99999862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:33.727554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.7439575 × 10-7
5-th percentile0.049573943
Q10.24979431
median0.49852298
Q30.74872122
95-th percentile0.9496059
Maximum0.99999862
Range0.99999785
Interquartile range (IQR)0.49892691

Descriptive statistics

Standard deviation0.28849949
Coefficient of variation (CV)0.5781848
Kurtosis-1.1981275
Mean0.49897453
Median Absolute Deviation (MAD)0.24943054
Skewness0.0046300914
Sum105959.24
Variance0.083231955
MonotonicityNot monotonic
2025-02-23T12:12:33.838592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07702502958 1
 
< 0.1%
0.4381752163 1
 
< 0.1%
0.852955162 1
 
< 0.1%
0.2258819761 1
 
< 0.1%
0.6218615289 1
 
< 0.1%
0.7529753021 1
 
< 0.1%
0.8866593829 1
 
< 0.1%
0.6768941385 1
 
< 0.1%
0.3487345531 1
 
< 0.1%
0.8681888647 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
7.743957466 × 10-71
< 0.1%
2.537464936 × 10-61
< 0.1%
5.941009967 × 10-61
< 0.1%
7.404393862 × 10-61
< 0.1%
1.202365004 × 10-51
< 0.1%
1.287837196 × 10-51
< 0.1%
4.869594218 × 10-51
< 0.1%
5.776587314 × 10-51
< 0.1%
6.048936703 × 10-51
< 0.1%
7.553404638 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999986241 1
< 0.1%
0.9999898946 1
< 0.1%
0.9999775677 1
< 0.1%
0.9999711264 1
< 0.1%
0.9999437912 1
< 0.1%
0.9999419087 1
< 0.1%
0.9999386562 1
< 0.1%
0.9999369062 1
< 0.1%
0.9999287185 1
< 0.1%
0.9999262206 1
< 0.1%

pita
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50115233
Minimum7.9775181 × 10-6
Maximum0.99999347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:34.070431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.9775181 × 10-6
5-th percentile0.050683607
Q10.25032151
median0.50222802
Q30.75121219
95-th percentile0.95093408
Maximum0.99999347
Range0.9999855
Interquartile range (IQR)0.50089068

Descriptive statistics

Standard deviation0.2888743
Coefficient of variation (CV)0.57642016
Kurtosis-1.2025394
Mean0.50115233
Median Absolute Deviation (MAD)0.25046699
Skewness-0.0026835509
Sum106421.7
Variance0.083448364
MonotonicityNot monotonic
2025-02-23T12:12:34.194452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4637661991 1
 
< 0.1%
0.283602689 1
 
< 0.1%
0.3212623222 1
 
< 0.1%
0.2186850894 1
 
< 0.1%
0.4570374186 1
 
< 0.1%
0.955232418 1
 
< 0.1%
0.5025110953 1
 
< 0.1%
0.653453409 1
 
< 0.1%
0.3370602587 1
 
< 0.1%
0.2088513633 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
7.977518126 × 10-61
< 0.1%
8.180784783 × 10-61
< 0.1%
8.534000501 × 10-61
< 0.1%
1.69545237 × 10-51
< 0.1%
2.131069445 × 10-51
< 0.1%
2.258408668 × 10-51
< 0.1%
2.298550121 × 10-51
< 0.1%
2.636952295 × 10-51
< 0.1%
2.696162304 × 10-51
< 0.1%
3.438315388 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999934731 1
< 0.1%
0.9999829516 1
< 0.1%
0.9999820996 1
< 0.1%
0.9999776178 1
< 0.1%
0.999975547 1
< 0.1%
0.999973749 1
< 0.1%
0.9999733255 1
< 0.1%
0.9999714195 1
< 0.1%
0.9999693917 1
< 0.1%
0.9999668731 1
< 0.1%

targetscan
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.501117
Minimum4.1364528 × 10-6
Maximum0.99999668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:34.313832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.1364528 × 10-6
5-th percentile0.050122576
Q10.25178984
median0.50181385
Q30.75186016
95-th percentile0.95036286
Maximum0.99999668
Range0.99999254
Interquartile range (IQR)0.50007032

Descriptive statistics

Standard deviation0.28867799
Coefficient of variation (CV)0.57606903
Kurtosis-1.2000675
Mean0.501117
Median Absolute Deviation (MAD)0.25003186
Skewness-0.0050861131
Sum106414.2
Variance0.083334981
MonotonicityNot monotonic
2025-02-23T12:12:34.422921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7651636218 1
 
< 0.1%
0.9282443263 1
 
< 0.1%
0.1561413195 1
 
< 0.1%
0.9269707731 1
 
< 0.1%
0.9061510761 1
 
< 0.1%
0.04937826627 1
 
< 0.1%
0.1097590095 1
 
< 0.1%
0.2483067053 1
 
< 0.1%
0.2265969148 1
 
< 0.1%
0.9442487152 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
4.136452795 × 10-61
< 0.1%
4.335402382 × 10-61
< 0.1%
4.461687015 × 10-61
< 0.1%
6.533224721 × 10-61
< 0.1%
1.218648272 × 10-51
< 0.1%
1.407818904 × 10-51
< 0.1%
1.808029239 × 10-51
< 0.1%
2.329879473 × 10-51
< 0.1%
2.517149427 × 10-51
< 0.1%
2.690187606 × 10-51
< 0.1%
ValueCountFrequency (%)
0.999996679 1
< 0.1%
0.9999911958 1
< 0.1%
0.9999892316 1
< 0.1%
0.9999883625 1
< 0.1%
0.9999881889 1
< 0.1%
0.9999857743 1
< 0.1%
0.9999807865 1
< 0.1%
0.9999792912 1
< 0.1%
0.999972055 1
< 0.1%
0.9999714264 1
< 0.1%

predicted.sum
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0103462
Minimum1.5824651 × 10-5
Maximum9.9999084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:34.528689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.5824651 × 10-5
5-th percentile0.50566384
Q12.5122413
median5.0181956
Q37.5088777
95-th percentile9.5032151
Maximum9.9999084
Range9.9998925
Interquartile range (IQR)4.9966365

Descriptive statistics

Standard deviation2.8844607
Coefficient of variation (CV)0.57570087
Kurtosis-1.197592
Mean5.0103462
Median Absolute Deviation (MAD)2.4983252
Skewness-0.0047710748
Sum1063967.1
Variance8.3201134
MonotonicityNot monotonic
2025-02-23T12:12:34.633582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.392973721 1
 
< 0.1%
4.324298833 1
 
< 0.1%
6.243358881 1
 
< 0.1%
2.650631148 1
 
< 0.1%
5.586710605 1
 
< 0.1%
2.906425036 1
 
< 0.1%
9.385978417 1
 
< 0.1%
2.275804283 1
 
< 0.1%
5.400942902 1
 
< 0.1%
3.256649819 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
1.582465119 × 10-51
< 0.1%
0.0001127681052 1
< 0.1%
0.0001258443752 1
< 0.1%
0.0001910987888 1
< 0.1%
0.0001976880087 1
< 0.1%
0.000262373681 1
< 0.1%
0.0003145903409 1
< 0.1%
0.0003205559806 1
< 0.1%
0.0003493989846 1
< 0.1%
0.0003699696828 1
< 0.1%
ValueCountFrequency (%)
9.999908374 1
< 0.1%
9.999802292 1
< 0.1%
9.999761352 1
< 0.1%
9.999679984 1
< 0.1%
9.999619268 1
< 0.1%
9.999538163 1
< 0.1%
9.999525225 1
< 0.1%
9.999501612 1
< 0.1%
9.999480194 1
< 0.1%
9.999463328 1
< 0.1%

all.sum
Real number (ℝ)

Unique 

Distinct212354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0000851
Minimum8.1482043 × 10-5
Maximum9.9999872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-02-23T12:12:34.740819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.1482043 × 10-5
5-th percentile0.50052138
Q12.5059411
median5.0035977
Q37.4931057
95-th percentile9.4958729
Maximum9.9999872
Range9.9999057
Interquartile range (IQR)4.9871646

Descriptive statistics

Standard deviation2.8826847
Coefficient of variation (CV)0.57652712
Kurtosis-1.1970479
Mean5.0000851
Median Absolute Deviation (MAD)2.4933649
Skewness-0.0023108606
Sum1061788.1
Variance8.309871
MonotonicityNot monotonic
2025-02-23T12:12:34.849571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.487877859 1
 
< 0.1%
7.666791428 1
 
< 0.1%
4.13362407 1
 
< 0.1%
3.070646043 1
 
< 0.1%
2.389189758 1
 
< 0.1%
3.035776214 1
 
< 0.1%
6.997966007 1
 
< 0.1%
9.420516518 1
 
< 0.1%
3.073373663 1
 
< 0.1%
1.482559286 1
 
< 0.1%
Other values (212344) 212344
> 99.9%
ValueCountFrequency (%)
8.148204313 × 10-51
< 0.1%
0.0001859963496 1
< 0.1%
0.0003688354131 1
< 0.1%
0.0003699593097 1
< 0.1%
0.000414254698 1
< 0.1%
0.0004323137693 1
< 0.1%
0.0004683989212 1
< 0.1%
0.0004721965414 1
< 0.1%
0.0005789316115 1
< 0.1%
0.0005834709547 1
< 0.1%
ValueCountFrequency (%)
9.999987182 1
< 0.1%
9.999951269 1
< 0.1%
9.999939693 1
< 0.1%
9.999878253 1
< 0.1%
9.999845089 1
< 0.1%
9.999826876 1
< 0.1%
9.999796655 1
< 0.1%
9.999788935 1
< 0.1%
9.999768537 1
< 0.1%
9.999766095 1
< 0.1%

label
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
191395 
1
20959 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters212354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 191395
90.1%
1 20959
 
9.9%

Length

2025-02-23T12:12:34.947292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T12:12:34.996856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 191395
90.1%
1 20959
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 191395
90.1%
1 20959
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 191395
90.1%
1 20959
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 191395
90.1%
1 20959
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 191395
90.1%
1 20959
 
9.9%

Interactions

2025-02-23T12:12:25.862221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:09.884375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.196501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.397352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.732831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.977318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.330279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.573200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.918617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.165637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.619040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.979622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.509536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.975525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:09.984798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.290004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.494525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.831334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.077468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.427592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.672067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.022871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.270735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.731660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:23.083388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.623385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.077874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.076936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.376922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.587612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.926858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.170276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.521463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.764291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.117502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.470652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.832475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:23.287192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.751687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.270257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.171686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.469754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.682708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.025177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.380269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.617773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.950712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.216037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.578589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.939660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:23.415717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.861251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.372745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.267325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.559789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.778318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.124561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.473596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.712457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.065307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.311007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.673116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.033830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:23.531151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.956702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.477542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.361378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.650630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.967568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.219590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.566906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.804627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.158460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.406415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.770226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.127512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:23.647461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.047196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.584458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.518494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.742683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.062867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.315335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.664548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.903625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.250609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.501651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.870565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.225215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:23.751538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.147684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.685610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.623547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.834141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.157363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.407803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.757715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.999588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.345765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.595630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.973129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.324180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:23.864960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.255814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.782158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.718045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.925799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.253688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.504240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.852101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.093704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.439728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.687722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.072697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.421091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:23.977858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.361600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.888438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.813139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.017802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.348115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.597959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:15.948019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.189847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.534912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.783316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.186383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.513790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.096223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.467497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:26.996246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:10.909003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.120563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.443404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.692678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.044092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.287141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.629393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.878102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.298320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.602611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.204201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.566952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:27.095114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.006277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.214979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.540492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.785867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.137648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.381901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.727529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:19.974522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.409187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.698580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.296222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.662428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:27.187920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:11.101375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:12.305576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:13.636937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:14.882800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:16.236899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:17.478786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:18.823237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:20.069043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:21.516810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:22.877467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:24.398759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T12:12:25.758971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-23T12:12:35.082809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agealcohol_consumptionall.sumbiopsy_resultsct_scandiana_microtdietary_habitselmmoendoscopic_imagesethnicityexisting_conditionsfamily_historygendergeographical_locationhelicobacter_pylori_infectionlabelmature_mirna_accmature_mirna_idmicrocosmmirandamirdbpictarpitapredicted.sumsmoking_habitstarget_ensembltarget_entreztarget_symboltargetscan
age1.0000.000-0.0010.0030.0060.0030.004-0.0010.0000.0000.0000.0000.0040.0020.0040.0000.0000.0020.0010.003-0.0000.000-0.0020.0010.0000.001-0.0000.004-0.000
alcohol_consumption0.0001.0000.0000.0030.0020.0000.0010.0040.0010.0000.0030.0000.0000.0040.0000.0000.0000.0000.0000.0030.0000.0050.0000.0040.0040.0000.0030.0020.003
all.sum-0.0010.0001.0000.0000.0010.0010.0000.0020.0000.0000.0030.0000.0000.0040.0060.0050.0000.0060.0000.0010.0010.003-0.001-0.0020.000-0.0010.0020.0000.001
biopsy_results0.0030.0030.0001.0000.0000.0070.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0040.0000.000
ct_scan0.0060.0020.0010.0001.0000.0000.0000.0030.0020.0040.0020.0000.0000.0000.0000.0000.0000.0020.0010.0040.0020.0070.0030.0040.0000.0000.0000.0000.004
diana_microt0.0030.0000.0010.0070.0001.0000.0070.0030.0000.0070.0000.0030.0000.0050.0000.0010.0000.000-0.003-0.003-0.001-0.0010.000-0.0010.0030.0000.0020.000-0.002
dietary_habits0.0040.0010.0000.0000.0000.0071.0000.0060.0030.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0050.0000.0020.0000.0000.0000.0000.0000.0040.000
elmmo-0.0010.0040.0020.0030.0030.0030.0061.0000.0050.0000.0060.0000.0050.0020.0050.0020.0000.000-0.002-0.001-0.001-0.0000.002-0.0030.0050.0020.0000.0040.001
endoscopic_images0.0000.0010.0000.0000.0020.0000.0030.0051.0000.0000.0000.0000.0000.0010.0000.0000.0020.0020.0060.0050.0020.0070.0000.0020.0000.0050.0000.0040.002
ethnicity0.0000.0000.0000.0000.0040.0070.0000.0000.0001.0000.0000.0040.0050.0040.0000.0000.0000.0030.0000.0000.0060.0020.0050.0020.0000.0060.0010.0010.001
existing_conditions0.0000.0030.0030.0000.0020.0000.0000.0060.0000.0001.0000.0000.0010.0000.0000.0020.0030.0000.0000.0000.0020.0050.0000.0000.0000.0030.0000.0000.000
family_history0.0000.0000.0000.0000.0000.0030.0000.0000.0000.0040.0001.0000.0030.0000.0000.0030.0040.0000.0000.0000.0000.0000.0000.0040.0030.0000.0000.0000.002
gender0.0040.0000.0000.0000.0000.0000.0000.0050.0000.0050.0010.0031.0000.0000.0020.0000.0000.0000.0000.0020.0000.0030.0000.0040.0000.0000.0000.0020.000
geographical_location0.0020.0040.0040.0000.0000.0050.0000.0020.0010.0040.0000.0000.0001.0000.0000.0000.0040.0000.0000.0050.0000.0000.0000.0000.0000.0000.0040.0000.002
helicobacter_pylori_infection0.0040.0000.0060.0000.0000.0000.0040.0050.0000.0000.0000.0000.0020.0001.0000.0010.0000.0030.0000.0040.0000.0000.0030.0040.0000.0000.0030.0000.000
label0.0000.0000.0050.0000.0000.0010.0000.0020.0000.0000.0020.0030.0000.0000.0011.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0030.0000.005
mature_mirna_acc0.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0030.0040.0000.0040.0000.0001.0000.0020.0040.0070.0030.0000.0000.0000.0000.0000.0040.0030.003
mature_mirna_id0.0020.0000.0060.0000.0020.0000.0000.0000.0020.0030.0000.0000.0000.0000.0030.0000.0021.0000.0010.0000.0000.0000.0030.0000.0000.0000.0000.0000.003
microcosm0.0010.0000.0000.0000.001-0.0030.000-0.0020.0060.0000.0000.0000.0000.0000.0000.0000.0040.0011.000-0.0020.002-0.000-0.001-0.0010.001-0.000-0.0010.0030.002
miranda0.0030.0030.0010.0000.004-0.0030.005-0.0010.0050.0000.0000.0000.0020.0050.0040.0000.0070.000-0.0021.000-0.0010.001-0.001-0.0000.0000.003-0.0040.0040.002
mirdb-0.0000.0000.0010.0000.002-0.0010.000-0.0010.0020.0060.0020.0000.0000.0000.0000.0000.0030.0000.002-0.0011.0000.001-0.001-0.0050.0000.0040.0020.005-0.000
pictar0.0000.0050.0030.0000.007-0.0010.002-0.0000.0070.0020.0050.0000.0030.0000.0000.0000.0000.000-0.0000.0010.0011.0000.002-0.0010.000-0.001-0.0020.0000.002
pita-0.0020.000-0.0010.0000.0030.0000.0000.0020.0000.0050.0000.0000.0000.0000.0030.0000.0000.003-0.001-0.001-0.0010.0021.000-0.0000.0000.003-0.0020.000-0.003
predicted.sum0.0010.004-0.0020.0000.004-0.0010.000-0.0030.0020.0020.0000.0040.0040.0000.0040.0000.0000.000-0.001-0.000-0.005-0.001-0.0001.0000.0010.001-0.0020.0030.000
smoking_habits0.0000.0040.0000.0000.0000.0030.0000.0050.0000.0000.0000.0030.0000.0000.0000.0020.0000.0000.0010.0000.0000.0000.0000.0011.0000.0060.0000.0000.000
target_ensembl0.0010.000-0.0010.0050.0000.0000.0000.0020.0050.0060.0030.0000.0000.0000.0000.0000.0000.000-0.0000.0030.004-0.0010.0030.0010.0061.0000.0020.0000.001
target_entrez-0.0000.0030.0020.0040.0000.0020.0000.0000.0000.0010.0000.0000.0000.0040.0030.0030.0040.000-0.001-0.0040.002-0.002-0.002-0.0020.0000.0021.0000.004-0.001
target_symbol0.0040.0020.0000.0000.0000.0000.0040.0040.0040.0010.0000.0000.0020.0000.0000.0000.0030.0000.0030.0040.0050.0000.0000.0030.0000.0000.0041.0000.000
targetscan-0.0000.0030.0010.0000.004-0.0020.0000.0010.0020.0010.0000.0020.0000.0020.0000.0050.0030.0030.0020.002-0.0000.002-0.0030.0000.0000.001-0.0010.0001.000

Missing values

2025-02-23T12:12:27.442146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-23T12:12:27.957793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agegenderethnicitygeographical_locationfamily_historysmoking_habitsalcohol_consumptionhelicobacter_pylori_infectiondietary_habitsexisting_conditionsendoscopic_imagesbiopsy_resultsct_scanmature_mirna_accmature_mirna_idtarget_symboltarget_entreztarget_ensembldiana_microtelmmomicrocosmmirandamirdbpictarpitatargetscanpredicted.sumall.sumlabel
043MaleEthnicity_AOther1000Low_SaltChronic GastritisNormalNegativeNegativeMIR123MIR123_1TP53503419466260.3813260.1870030.7864220.2048160.5619200.4381750.2836030.9282444.3242997.6667910
186FemaleEthnicity_BCalifornia1001High_SaltDiabetesNormalNegativeNegativeMIR123MIR234_2TP53690115031780.9585610.4933220.9639890.4980410.9855850.1446090.3753750.1035737.9676741.4832800
268MaleEthnicity_ACalifornia0110High_SaltNaNNormalNegativeNegativeMIR345MIR345_3KRAS401417879090.2695370.5735600.6668960.5403880.9058530.8272790.3509150.1668783.7486513.0467830
357FemaleEthnicity_AOther0001High_SaltChronic GastritisNormalNegativeNegativeMIR123MIR123_1KRAS735113107660.3722870.2613990.9494880.1341700.4299350.9352310.7947040.8670365.4782988.8113070
433MaleEthnicity_ACalifornia0110High_SaltDiabetesAbnormalNegativeNegativeMIR345MIR123_1CDH1798212770580.9746560.7544780.2631640.8767670.6508320.3376690.4274920.9158041.8091810.3946320
533FemaleEthnicity_CCalifornia1010High_SaltChronic GastritisAbnormalNegativeNegativeMIR123MIR234_2TP53485816659850.9069340.0507140.9544980.9285610.3498690.7683950.3578060.4647724.4704958.4785750
626MaleEthnicity_BCalifornia0100High_SaltChronic GastritisAbnormalNegativeNegativeMIR123MIR123_1KRAS619213492660.3873000.1592880.9735910.7555240.1934660.9028050.1172610.8847404.0222349.7445910
779MaleEthnicity_BCalifornia1000High_SaltDiabetesNormalNegativePositiveMIR123MIR123_1CDH1224910709860.0678390.8955010.4832890.0474870.7476120.0205670.3761790.2362409.2206841.0841670
858MaleEthnicity_ACalifornia1001High_SaltNaNNormalNegativeNegativeMIR123MIR123_1TP53530517828370.9189270.9341110.3892570.6907650.2283210.3486820.1804230.1614335.4901609.5280530
966FemaleEthnicity_BCalifornia0111High_SaltChronic GastritisNormalNegativePositiveMIR123MIR123_1TP53882818950300.4645530.4674000.9925380.6104100.2040090.3393470.7905070.9231555.3129606.5091981
agegenderethnicitygeographical_locationfamily_historysmoking_habitsalcohol_consumptionhelicobacter_pylori_infectiondietary_habitsexisting_conditionsendoscopic_imagesbiopsy_resultsct_scanmature_mirna_accmature_mirna_idtarget_symboltarget_entreztarget_ensembldiana_microtelmmomicrocosmmirandamirdbpictarpitatargetscanpredicted.sumall.sumlabel
21234470FemaleEthnicity_ACalifornia1011High_SaltChronic GastritisNormalNegativeNegativeMIR123MIR123_1TP53461711210040.2140530.9852990.0305050.4761530.7490600.9226300.2312770.8917368.5814662.3032120
21234540MaleEthnicity_ACalifornia0000High_SaltDiabetesNormalNegativeNegativeMIR123MIR123_1TP53731118560090.8546850.3890230.5793500.8317560.9872000.4360730.0966880.7245732.9142341.9128061
21234632MaleEthnicity_BCalifornia0100High_SaltDiabetesNormalNegativeNegativeMIR123MIR123_1TP53745518095200.0407210.7071630.0654860.3453930.4858520.7794210.0959270.1586545.7939988.9481750
21234727MaleEthnicity_AOther0011High_SaltChronic GastritisAbnormalNegativeNegativeMIR123MIR123_1TP53657913983350.8477330.4507730.5458280.7396070.9297640.1316520.8807510.1345663.6103288.2530890
21234849FemaleEthnicity_CCalifornia0111High_SaltChronic GastritisNormalNegativeNegativeMIR123MIR123_1TP53504615820470.7609670.7132260.4323000.5072130.6898940.9985060.5698850.9780472.7954023.9355880
21234947MaleEthnicity_ACalifornia0110High_SaltChronic GastritisAbnormalNegativeNegativeMIR123MIR123_1KRAS693419802810.9721930.4081230.9250350.2676280.5384740.4634710.8278920.9291470.8662606.6983030
21235059FemaleEthnicity_ACalifornia0110High_SaltChronic GastritisNormalNegativeNegativeMIR123MIR123_1TP53658112876970.9882440.5782040.7682790.3391680.2365230.4442160.0117620.0530985.1022162.3380170
21235158FemaleEthnicity_CCalifornia0000High_SaltNaNAbnormalNegativeNegativeMIR123MIR123_1CDH1992211152990.9524030.9409360.8652210.4481890.8993510.7666150.0984540.3840197.7746124.6016371
21235277FemaleEthnicity_BCalifornia1101High_SaltChronic GastritisAbnormalNegativeNegativeMIR123MIR123_1KRAS755814150230.9247840.4980880.0481950.9246100.1015210.4649880.6394010.4103272.3626982.0200470
21235371MaleEthnicity_ACalifornia0001High_SaltNaNNormalPositiveNegativeMIR123MIR345_3KRAS342015471420.1469550.6896960.7015240.2509550.1024580.0770250.4637660.7651642.3929744.4878780